SoroushVahidi/scidocs huggingface dataset:
Maintenance scheduling of geographically distributed assets with prognostics information
(2015)

Maintenance scheduling for high value assets has been studied for decades and is still a crucial area of research with new technological advancements. The main dilemma of maintenance scheduling is to avoid failures while preventing unnecessary maintenance. The technological advancements in real time monitoring and computational science make tracking asset health and forecasting asset failures possible. The usage and maintenance of assets can be planned more efficiently with the forecasted failure probability and remaining useful life (i.e., prognostic information). The prognostic information is time sensitive. Geographically distributed assets such as off-shore wind farms and railway switches add another complexity to the maintenance scheduling problem with the required time of travel to reach these assets. Thus, the travel time between geographically distributed assets should be incorporated in the maintenance scheduling when one technician (or team) is responsible for the maintenance of multiple assets. This paper presents a methodology to schedule the maintenance of geographically distributed assets using their prognostic information. Genetic Algorithm based solution incorporating the daily work duration of the maintenance team is also presented in the paper. © 2015 Elsevier B.V. All rights reserved.
PNUTS: Yahoo!'s hosted data serving platform
(2008)

We describe PNUTS, a massively parallel and geographically distributed database system for Yahoo!’s web applications. PNUTS provides data storage organized as hashed or ordered tables, low latency for large numbers of concurrent requests including updates and queries, and novel per-record consistency guarantees. It is a hosted, centrally managed, and geographically distributed service, and utilizes automated load-balancing and failover to reduce operational complexity. The first version of the system is currently serving in production. We describe the motivation for PNUTS and the design and implementation of its table storage and replication layers, and then present experimental results.
Performance Analysis of IEEE 802.11ac DCF with Hidden Nodes
(2012)

Recently, the IEEE 802.11 standard based Wireless Local Area Networks (WLAN) have become more popular and are widely deployed. It is anticipated that WLAN will play an important rule in the future wireless communication systems in order to provide several gigabits data rate. IEEE 802.11ac is one of the ongoing WLAN standard aiming to support very high throughput (VHT) with data rate of up to 6 Gbps below the 6 GHz band. In the development of IEEE 802.11ac standard, several new physical layer (PHY) and medium access control layer (MAC) features are taken into consideration, such as employing wider bandwidth in PHY and incrementing the limits of frame aggregation in MAC. However, due to the newly introduced features, some traditional techniques used in previous standards could face some problems. This paper presents a performance analysis of 802.11ac Distributed Coordination Function (DCF) in presence of hidden nodes in overlapping BSS (OBSS) environment. The effectiveness of DCF in IEEE 802.11ac WLAN when using different primary channels and different frequency bandwidth has also been discussed. Our results indicate that the traditional RTS/CTS handshake mechanism faces shortcomings and needs to be modified in order to support the newly defined 802.11ac amendment.
Real time lane detection for autonomous vehicles
(2008)

An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehiclepsilas heading direction). One of the principal approaches to detect road boundaries and lanes using vision system on the vehicle. However, lane detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes. Using a pair of hyperbolas which are fitting to the edges of the lane, those lanes are extracted using Hough transform. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.
Cloud detection of remote sensing images by deep learning
(2016)

Cloud detection plays a major role for remote sensing image processing. Most of the existed cloud detection methods use the low-level feature of the cloud, which often cause error result especially for thin cloud and complex scene. In this paper, a novel cloud detection method based on deep learning framework is proposed. The designed deep Convolutional Neural Networks (CNNs) consists of four convolutional layers and two fully-connected layers, which can mine the deep features of cloud. The image is firstly clustered into superpixels as sub-region through simple linear iterative cluster (SLIC) method. Through the designed network model, the probability of each superpixel that belongs to cloud region is predicted, so that the cloud probability map of the image is generated. Lastly, the cloud region is obtained according to the gradient of the cloud map. Through the proposed method, both thin cloud and thick cloud can be detected well, and the result is insensitive to complex scene. Experimental results indicate that the proposed method is more robust and effective than compared methods.
A Case for Protecting Computer Games With SGX
(2016)

The integrity and confidentiality of computer games has long been a concern of game developers, both in preventing players from cheating and from obtaining unlicensed copies of the software. Recently, Intel released SGX, which can provide new security guarantees for software developers to achieve an unprecedented level of software integrity and confidentiality. To explore how SGX can protect a computer game in practice, in this paper we make a first step of exploring new ways to protect the integrity and confidentiality of game code and data, and in doing so we have developed a framework and design principles for integrating games with SGX. We have applied our framework to demonstrate how it can be used to protect a real world computer game.
Semantic Web Services-A Survey
(2008)

The technology where the meaning of the information and the service of the web is defined by making the web to understand and satisfies the request of the people is called Semantic Web Services. That is the idea of having data on the web defined and linked in a way that it can be used by machines not just for display purpose, but for automation, integration and reuse of data across various application .The idea of the semantic is raised to overcome the limitation of the Web services such as Average WWW searches examines only about 25% of potentially relevant sites and return a lot of unwanted information, Information on web is not suitable for software agent and Doubling of size. It is built on top of the Web Services extended with rich semantic representations along with capabilities for automatic reasoning developed in the field of artificial intelligence. This survey attempts to give an overview of the underlying concepts and technologies along with the categorization, selection and discovery of services based on semantic.
High quality lip-sync animation for 3D photo-realistic talking head
(2012)

We propose a new 3D photo-realistic talking head with high quality, lip-sync animation. It extends our prior high-quality 2D photo-realistic talking head to 3D. An a/v recording of a person speaking a set of prompted sentences with good phonetic coverage for ~20-minutes is first made. We then use a 2D-to-3D reconstruction algorithm to automatically adapt a general 3D head mesh model to the person. In training, super feature vectors consisting of 3D geometry, texture and speech are augmented together to train a statistical, multi-streamed, Hidden Markov Model (HMM). The HMM is then used to synthesize both the trajectories of head motion animation and the corresponding dynamics of texture. The resultant 3D talking head animation can be controlled by the model predicted geometric trajectory while the articulator movements, e.g., lips, are rendered with dynamic 2D texture image sequences. Head motions and facial expression can also be separately controlled by manipulating corresponding parameters. In a real-time demonstration, the life-like 3D talking head can take any input text, convert it into speech and render lip-synced speech animation photo-realistically.
A Review of Human Activity Recognition Methods
(2015)

Recognizing human activities from video sequences or still images is a challenging task due to problems, such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of human activity classification. We propose a categorization of human activity methodologies and discuss their advantages and limitations. In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested in. Moreover, we provide a comprehensive analysis of the existing, publicly available human activity classification datasets and examine the requirements for an ideal human activity recognition dataset. Finally, we report the characteristics of future research directions and present some open issues on human activity recognition.
A Framework for Network Function Virtualization by Chang Lan Research Project
(2016)

The proliferation of network processing appliances (“Middleboxes”) has been accompanied by a growing recognition of the problems they bring, including expensive hardware and complex management. This recognition led the networking industry to launch a concerted effort towards Network Function Virtualization (NFV) with the goal of bringing greater openness and agility to network dataplanes. However, a closer look under the hood reveals a less rosy picture: NFV is currently replacing, on a one-to-one basis, monolithic hardware with monolithic software. Furthermore, while several efforts are exploring a new model for middlebox deployment in which a third-party offers middlebox processing as a service, no solutions address the confidentiality concerns. In order to process an organizations traffic, the cloud sees the traffic unencrypted. This means that the cloud now has access to potentially sensitive packet payloads and headers. In the first part of this thesis, we present E2, a scalable and application-agnostic scheduling framework for packet processing, and compare its performance to current approaches. E2 brings two benefits: (i) it allows developers to rely on external frameworkbased mechanisms for common tasks, freeing them to focus on their core application logic and (ii) it simplifies the operators responsibilities, as it both automates and consolidates common management tasks. In the following chapter, we then present Embark, the first system that enables a cloud provider to support middlebox outsourcing while maintaining the clients confidentiality. Embark supports a wide-range of middleboxes. Our evaluation shows that Embark supports these applications with competitive performance.
A Novel System for Visual Navigation of Educational Videos Using Multimodal Cues
(2017)

With recent developments and advances in distance learning and MOOCs, the amount of open educational videos on the Internet has grown dramatically in the past decade. However, most of these videos are lengthy and lack of high-quality indexing and annotations, which triggers an urgent demand for efficient and effective tools that facilitate video content navigation and exploration. In this paper, we propose a novel visual navigation system for exploring open educational videos. The system tightly integrates multimodal cues obtained from the visual, audio and textual channels of the video and presents them with a series of interactive visualization components. With the help of this system, users can explore the video content using multiple levels of details to identify content of interest with ease. Extensive experiments and comparisons against previous studies demonstrate the effectiveness of the proposed system.
Musical Instrument Recognition in User-generated Videos using a Multimodal Convolutional Neural Network Architecture
(2017)

This paper presents a method for recognizing musical instruments in user-generated videos. Musical instrument recognition from music signals is a well-known task in the music information retrieval (MIR) field, where current approaches rely on the analysis of the good-quality audio material. This work addresses a real-world scenario with several research challenges, i.e. the analysis of user-generated videos that are varied in terms of recording conditions and quality and may contain multiple instruments sounding simultaneously and background noise. Our approach does not only focus on the analysis of audio information, but we exploit the multimodal information embedded in the audio and visual domains. In order to do so, we develop a Convolutional Neural Network (CNN) architecture which combines learned representations from both modalities at a late fusion stage. Our approach is trained and evaluated on two large-scale video datasets: YouTube-8M and FCVID. The proposed architectures demonstrate state-of-the-art results in audio and video object recognition, provide additional robustness to missing modalities, and remains computationally cheap to train.
VINERy: A Visual IDE for Information Extraction
(2015)

Information Extraction (IE) is the key technology enabling analytics over unstructured and semi-structured data. Not surprisingly, it is becoming a critical building block for a wide range of emerging applications. To satisfy the rising demands for information extraction in real-world applications, it is crucial to lower the barrier to entry for IE development and enable users with general computer science background to develop higher quality extractors. In this demonstration, we present VINERY, an intuitive yet expressive visual IDE for information extraction. We show how it supports the full cycle of IE development without requiring a single line of code and enables a wide range of users to develop high quality IE extractors with minimal efforts. The extractors visually built in VINERY are automatically translated into semantically equivalent extractors in a state-of-the-art declarative language for IE. We also demonstrate how the auto-generated extractors can then be imported into a conventional Eclipse-based IDE for further enhancement. The results of our user studies indicate that VINERY is a significant step forward in facilitating extractor development for both expert and novice IE developers.
Modeling and Analyzing the Reliability and Cost of Service Composition in the IoT: A Probabilistic Approach
(2012)

Recently, many efforts have been devoted to explore the integration of Internet of Things (IoT) and Service-Oriented Computing (SOC). These works allow the real-world devices to provide their functionality as web services. However, two important issues, unreliable service providing and resource constraints, make the modeling and analysis of service composition in IoT a big challenge. In this paper, we propose a probabilistic approach to formally describe and analyze the reliability and cost-related properties of the service composition in IoT. First, a service composition in IoT is modeled as a finite state machine (FSM) which focuses on the functional part. Then, we extend this FSM model to a Markov Decision Process (MDP), which can specify the reliability of service operations. Furthermore, we extend MDP with cost structure, which can represent the different service quality attributes for each operation, such as energy consumption, communication cost, etc. The desirable quality properties of the service composition are specified by a probabilistic extension of temporal logic PCTL. We adopt a well-established probabilistic model checker PRISM to verify and analyze those properties of our service composition models.
A Simulink-based robotic toolkit for simulation and control of the PUMA 560 robot manipulator
(2001)

In this paper, a Simulink Robotic Toolkit (SRTK) for the Puma 560 robot manipulator is developed on the MATLAB/SIMULINK-based platform. Through the use of the Real-Time Linux Target and the Real-Time Windows Target, the SRTK can be executed on the Linux or Win32-based operating systems (e.g., Windows 95/98/NT) in real-time. Moreover, the graphical user-friendly nature of Simulink allows the SRTK to be a flexible tool that can easily be customized to fit the specific needs of the user. That is, based on the layered approach of the SRTK, the user can perform operations such as calibration, joint control, Cartesian control, Cartesian PD control, impedance control, some trajectory generation tasks, and real-time simulation of the Puma 560 through a user-friendly MATLAB-based graphical user interface (GUI) without writing any code. One of the key features of the toolkit is that users can easily incorporate additional functionality and hardware through the simple block diagram interface that Simulink provides. Moreover, the MATLABbased GUI can also easily be modified to allow the user to exploit the added functionality with the GUI by using straightforward MATLAB script files. Hence, the SRTK allows a researcher to use the Puma 560 without the burden of the external issues related to the control, interface, and software issues, while providing for the flexibility for easily modifying components for increased functionality.
A compendium of RNA-binding motifs for decoding gene regulation
(2013)

RNA-binding proteins are key regulators of gene expression, yet only a small fraction have been functionally characterized. Here we report a systematic analysis of the RNA motifs recognized by RNA-binding proteins, encompassing 205 distinct genes from 24 diverse eukaryotes. The sequence specificities of RNA-binding proteins display deep evolutionary conservation, and the recognition preferences for a large fraction of metazoan RNA-binding proteins can thus be inferred from their RNA-binding domain sequence. The motifs that we identify in vitro correlate well with in vivo RNA-binding data. Moreover, we can associate them with distinct functional roles in diverse types of post-transcriptional regulation, enabling new insights into the functions of RNA-binding proteins both in normal physiology and in human disease. These data provide an unprecedented overview of RNA-binding proteins and their targets, and constitute an invaluable resource for determining post-transcriptional regulatory mechanisms in eukaryotes.
MPLS based hybridization in SDN
(2017)

The new paradigm of Software Defined Networking (SDN) although has great potential to address the complex problems presented by enterprise networks, it has its own deployment and scalability issues. Further, a full SDN deployment has its own business and economic challenges. A smooth transition from legacy networks to SDN (disruption free, accommodating budget constraints, with progressive improvement in network management) requires a hybrid networking model as an inevitable intermediate step; that allows heterogeneous paradigms to function together while the full transition is realized in phases. Therefore, the need of the hour is to develop an incremental deployment strategy that caters to the needs of the organization. We present here a class-based hybrid SDN model for Multi Protocol Label Switching (MPLS) networks. We discuss the model, design, components, their interactions, advantages and drawbacks. We also present a n implementation and evaluation of a prototype. In legacy networks, MPLS architecture closely resembles SDN paradigm in terms of separation of control and data planes, flow-abstraction etc. Moreover, ISPs have preferred MPLS over the years due to benefits of virtual private networks and traffic engineering. The central idea is to partition traffic using forwarding equivalence classes at the ingress router, the rules of which can be updated via a centralized controller using OpenFlow. Therefore, we aim to use the standard MPLS data-plane together with a control-plane based on OpenFlow to come up with a systematic incremental deployment methodology as well as a hybrid operation model
Accurate and efficient stereo processing by semi-global matching and mutual information
(2005)

This paper considers the objectives of accurate stereo matching, especially at object boundaries, robustness against recording or illumination changes and efficiency of the calculation. These objectives lead to the proposed semi-global matching method that performs pixelwise matching based on mutual information and the approximation of a global smoothness constraint. Occlusions are detected and disparities determined with sub-pixel accuracy. Additionally, an extension for multi-baseline stereo images is presented. There are two novel contributions. Firstly, a hierarchical calculation of mutual information based matching is shown, which is almost as fast as intensity based matching. Secondly, an approximation of a global cost calculation is proposed that can be performed in a time that is linear to the number of pixels and disparities. The implementation requires just 1 second on typical images.
Evaluation of Cost Functions for Stereo Matching
(2007)

Stereo correspondence methods rely on matching costs for computing the similarity of image locations. In this paper we evaluate the insensitivity of different matching costs with respect to radiometric variations of the input images. We consider both pixel-based and window-based variants and measure their performance in the presence of global intensity changes (e.g., due to gain and exposure differences), local intensity changes (e.g., due to vignetting, non-Lambertian surfaces, and varying lighting), and noise. Using existing stereo datasets with ground-truth disparities as well as six new datasets taken under controlled changes of exposure and lighting, we evaluate the different costs with a local, a semi-global, and a global stereo method.